A novel strategy for multi-resource load balancing in agent-based systems

TL;DR

This paper introduces a new multi-resource load balancing strategy for agent-based systems that leverages agent social behavior and adaptation to optimize complex enterprise architectures.

Contribution

It proposes a novel load balancing approach utilizing agent social behavior and self-assessment, with implementation and experimental validation.

Findings

Effective load balancing in agent systems demonstrated

Improved system adaptability and resource utilization

Experimental results confirm the strategy's viability

Abstract

The paper presents a multi-resource load balancing strategy which can be utilised within an agent-based system. This approach can assist system designers in their attempts to optimise the structure for complex enterprise architectures. In this system, the social behaviour of the agent and its adaptation abilities are applied to determine an optimal setup for a given configuration. All the methods have been developed to allow the agent's self-assessment. The proposed agent system has been implemented and the experiment results are presented here.